基于地面特征的越野环境轻量化回环检测

The traditional loop closure detection algorithm relies on the accuracy of odometer and the external global positioning information,which consumes too much computing resources,and the existing lightweight loop closure detection algorithm has poor translation invariance and difficulty in adapting to the sparse environmental characteristics in off-road road environment. In order to improve the positioning capability of unmanned platform in the condition of satellite rejection for a long time and a large range of tasks,a lightweight loop closure detection algorithm using light detection and ranging (LiDAR) point clouds to describe the ground feature is proposed. It is different from extracting the point cloud features from single or multi-frame point clouds by deep learning. And a global descriptor is constructed. The fast LiDAR point clouds ground feature description approach is used to achieve the fast feature extraction of single frame point cloud and the globally consistent position feature description,and the multi-frame LiDAR point clouds ground features are aggregated into the sub-map loop closure detection descriptors. A lightweight global descriptor is constructed by odometer pose between adjacent frames,and the global descriptors are matched and the loop closure detection is realized without prior position information. The proposed algorithm is verified by using the mechanical and solid-state LiDAR in off-road environment. Compared with the existing lightweight loop closure detection algorithms,the proposed algorithm has the advantages of high recall rate,good real-time performance and less resource consumption in the off-road environment.